Final Project Proposal:
Examining the Relationship between Green Space Access, Income, and Health

4 November 2025

Authors¶

Patrick A. Mikkelsen // Student Number: 1010572514 // patrick.mikkelsen@utoronto.ca

Mike McCracken // Student Number: [add your student number please] // mike.mccracken@utoronto.ca

Hugo Cheng // Student Number: 1009827323 // hugo.cheng@mail.utoronto.ca

Course Information¶

Instructor: Professor Ignacio Tiznado-Aitken

Course: GGR375H1 F: Introduction to Programming in GIS

TA: Evan Powers

1. Introduction and Research Question¶

Previous literature has shown that Toronto's low-income and racialized communities have less access to trees, parks, and green space (Ward 2020, Pinault et al. 2021, Vabi 2022). We aim to examine the relationship between population demographics, income, and access to green space across Toronto.

2. Background and Motivation¶

[Explain why this topic is important and what gap in knowledge you're addressing]

3. Data Sources¶

[List the datasets you plan to use, including:

  • Dataset names
  • Sources/URLs
  • Spatial resolution
  • Temporal coverage
  • Key variables]

Required Libraries¶

This analysis utilizes Python libraries for geospatial analysis (GeoPandas), statistical computing (SciPy, NumPy), data manipulation (Pandas), and visualization (Matplotlib, Seaborn, Folium).

In [1]:
import geopandas as gpd
import pandas as pd
import shapely as shp
import folium
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns

# Set visualization style
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 6)
plt.rcParams['font.size'] = 10
In [ ]:
census_boundaries_path = "Green Space & Income/data/ldb_000a21a_e/ldb_000a21a_e.shp"
census_data_path = "Green Space & Income/data/98-401-X2021007_eng_CSV/98-401-X2021007_English_CSV_data.csv"
census_sd_path = "Green Space & Income/data/lcsd000b21a_e/lcsd000b21a_e.shp"

Census Tract Boundaries and Spatial Delimitation to Toronto¶

Census tract boundaries from Statistics Canada's 2021 Census are loaded and transformed to the WGS84 coordinate reference system (EPSG:4326). Census tracts represent small, relatively stable geographic units designed to be homogeneous with respect to population characteristics, economic status, and living conditions.

To focus the analysis on Toronto proper (Census Subdivision Code 3520005), census tract boundaries are immediately clipped to the municipal boundary, excluding surrounding municipalities within the Greater Toronto Area.

In [ ]:
# Load census tract boundaries
census_boundaries = gpd.read_file(census_boundaries_path)
census_boundaries = census_boundaries.to_crs(epsg=4326)

# Load census subdivision boundaries and subset to Toronto
csd_boundaries = gpd.read_file(census_sd_path)
toronto_csd = csd_boundaries[csd_boundaries['CSDUID'] == '3520005'].copy()

# Ensure CRS match
if toronto_csd.crs != census_boundaries.crs:
    toronto_csd = toronto_csd.to_crs(census_boundaries.crs)

# Clip census boundaries to Toronto
toronto_boundaries = gpd.clip(census_boundaries, toronto_csd)
toronto_boundaries = toronto_boundaries.to_crs(epsg=4326)

# Filter to only polygon/multipolygon geometries (exclude any line features)
toronto_boundaries = toronto_boundaries[toronto_boundaries.geometry.type.isin(['Polygon', 'MultiPolygon'])].copy()

# Save the Toronto census tracts
toronto_boundaries.to_file("my_data/toronto_census_tracts.shp")

print(f"Loaded {len(census_boundaries)} census tracts total")
print(f"Subset to {len(toronto_boundaries)} census tracts in Toronto")to

Loaded 6247 census tracts total
Subset to 585 census tracts in Toronto

You're gonna wanna restart your kernel now...

In [2]:
toronto_boundaries = gpd.read_file("my_data/toronto_census_tracts.shp")
toronto_boundaries.explore()
Out[2]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Census Demographic and Economic Data¶

The 2021 Census Profile dataset contains demographic and socioeconomic characteristics for all census geographic areas in Canada. Census tract-level records are extracted for subsequent analysis.

In [7]:
census_data = pd.read_csv(census_data_path, 
                          encoding="latin1", low_memory=False)
ct_data = census_data[census_data['GEO_LEVEL'] == 'Census tract'].copy()

The Kernel crashed while executing code in the current cell or a previous cell. 

Please review the code in the cell(s) to identify a possible cause of the failure. 

Click <a href='https://aka.ms/vscodeJupyterKernelCrash'>here</a> for more info. 

View Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details.

Population and Income Variables¶

Population counts and median household income data are extracted from the census profile. These variables serve as the dependent and independent measures for analyzing green space equity.

In [ ]:
population_data = toronto_ct_data[
    (toronto_ct_data['CHARACTERISTIC_NAME'] == 'Population, 2021')
][['DGUID', 'C1_COUNT_TOTAL']].copy()
population_data.rename(columns={'C1_COUNT_TOTAL': 'POPULATION'}, inplace=True)
population_data['POPULATION'] = pd.to_numeric(population_data['POPULATION'], errors='coerce')

income_data = toronto_ct_data[
    toronto_ct_data['CHARACTERISTIC_NAME'].str.contains('Median total income of household', case=False, na=False)
][['DGUID', 'C1_COUNT_TOTAL']].copy()
income_data.rename(columns={'C1_COUNT_TOTAL': 'MEDIAN_INCOME'}, inplace=True)
income_data['MEDIAN_INCOME'] = pd.to_numeric(income_data['MEDIAN_INCOME'], errors='coerce')

Green Space Data¶

Municipal green space data includes parks, ravines, golf courses, and other vegetated areas within Toronto's boundaries. Spatial data are standardized to the WGS84 coordinate system for compatibility with census boundaries.

In [ ]:
green_spaces = gpd.read_file("Green Spaces - 4326/Green Spaces - 4326.shp")

4. Methodology¶

[Outline your analytical approach:

  • Data preprocessing steps
  • Spatial analysis techniques
  • Statistical methods
  • Python packages you'll use (e.g., geopandas, rasterio, scikit-learn)]

5. Expected Outputs¶

[Describe what you plan to produce:

  • Maps/visualizations
  • Statistical results
  • Python scripts/modules]

6. Timeline¶

[Break down the project into tasks with tentative deadlines]

7. Challenges and Limitations¶

[Identify potential obstacles and how you plan to address them]

8. References¶

Pinault, Lauren, Tanya Christidis, Olaniyan Toyib, and Dan L. Crouse. 2021. “Ethnocultural and Socioeconomic Disparities in Exposure to Residential Greenness within Urban Canada.” Health Reports (Ottawa, Canada) 32 (5): 3–14. https://doi.org/10.25318/82-003-x202100500001-eng.

Vabi, Vilbert. 2022. “Parks and Forests Are Missing in Marginalized Neighbourhoods.” Nature Canada, March 18. https://naturecanada.ca/news/blog/parks-and-forests-are-missing-in-marginalized-neighbourhoods/.

Ward, Christine. 2020. “Toronto’s Low-Income and Racialized Communities Have Fewer Trees: U of T Researchers | University of Toronto.” News. U of T News, October 26. https://www.utoronto.ca/news/toronto-s-low-income-and-racialized-communities-have-fewer-trees-u-t-researchers.